As the job title implies, data architects are responsible for designing, deploying, and maintaining an organization’s data architecture. As they do so, they must stay aware of how different teams will store, upload, and consume that data stored within the framework.
According to Burning Glass, which collects and analyzes millions of job postings from across the country, data architect jobs are in demand, with the role projected to grow 9.3 percent over the next decade (although one of the experts we interviewed seems to disagree with that idea; see below). The average time to fill an open position is 41 days, indicating a lot of hungry employers having difficulties finding available data architects.
It’s also a pretty lucrative position, with Burning Glass plugging the median data architect salary at $115,379. In order to land that sort of paycheck, though, you’ll need the right combination of skills, including SQL, data warehousing, and data management.
Dice Insights spoke with Anne Marie Smith, PhD, vice president of education and chief methodologist for EWSolutions, about the challenges faced by data architects today, why it’s critical to be confident in communication skills, and the three books they need to know before going into an interview.
What are some of the challenges faced by data architects today?
Smith noted most organizations are moving away from traditional data architecture, since so many enterprises use commercial off-the-shelf software and don’t feel the need for a custom architecture at the development stage.
As a result, data architects often find themselves assigned data integration tasks, such as how to integrate two or more packages into an organization’s database, how to re-design a database to fit new software, or how to expand requirements in databases that already exist.
“The view of data architecture as a process that starts at the analysis and requirements stage is not nearly as heavily practiced as it was years ago, and they’re losing their positions, which makes it a more competitive field overall, with experienced people competing for fewer jobs,” she explained.
What are some technical questions interviewees are likely to be asked?
Smith said this depends heavily on the environment in the interviewing organization; most data architects should be asked detailed questions on conceptual and logical data modeling, conceptual to physical model transformation, the value of physical and conceptual metadata, and what architecture and modeling software they’re most comfortable handling.
From there, the questions will evolve to focus on the company’s chosen software. For example, a company might have embraced SAP PowerDesigner or Erwin, so questions will focus on those platforms. Do as much research as you can before the interview to figure out the composition of the company’s tech stack—sometimes a corporate website or blog will reveal that information, and sometimes you may need to ask an employee or your interviewer before heading into the interview itself.
“The technical questions around the data modeling software will be different, which means the data architect must be conversant with the software architecture the organization is using,” she said.
What are the most important skillsets a data architect should have?
“Quite a few,” Smith explained. “They must be technically competent in data architecture modeling best practices, they must be technically competent in use of chosen data architecture modeling software, and they should have a better than average understanding of the business environment in which they’re working with.”
They should have business data definition skills, as well as “more than a passing knowledge” of SQL for queries.
“They have to be really detail-oriented, but not only do they have to have that, they have to have the ability to see the big picture, across the whole project, the whole subject matter, or even at the enterprise level—they have to have that balance,” Smith added.
The last thing they need to have is communication, facilitation and interviewing skills (i.e., “soft skills”), because data architects are always dealing with people, gathering requirements for data models.
“The data architect must be able to speak to two communities, the business and the technical, and if they don’t have those communications skills, they won’t be able to ask the right questions, and translate those requirements into something tangible,” she said. “Many times they are called upon to present their results—I can’t hire a DA who’s terrified of speaking in front of a lot of people.”
How can one best prepare for a data architect interview?
“Review your experiences. I hope you have a resume, and has enough detail to jog your memory about the tasks you’ve performed,” Smith noted.
She recommends writing out your experience: You want to take the bullet point on your resume and expand it so that you know how to speak to a question such as, “You’ve done a complete enterprise logical data model—how did you do that?”
It’s also a good idea to practice using the software that the company utilizes, as many second interviews will expect some level of skill determination. “Go back to your Erwin instance and practice with the software, so when the interview asks you how to develop a relationship, you can walk the interviewer through the steps the software would take you through,” she noted.
What questions should the interviewee ask?
The interviewee should ask about the company’s environment, especially concerning data development, data architecture, and what the company’s view is in those areas. For example:
- Do they have an enterprise data management initiative?
- What’s the company’s philosophy on data architecture? Is it company-wide, business unit-based?
- Is it data architecture from conceptual and logical view as well as physical?
- Do they write some of their own software, or are they generally commercial off-the-shelf software purchasers who expect data architects to be data integrators?
“Last but not least, ask how the organization views the relationship between business and IT,” Smith said. “If it’s all done in IT with little business involvement, there is little opportunity for the data architect to gather the business requirements to the level needed to substantiate a viable data architecture model.”